This study is about the use of mixture to model behavioral and cognitive data measured repeatedly across development in children. Estimation of multinormal mixture models using the EM algorithm is explained in detail. This algorithm simplifies computation of mixture models because the parameters in each group are estimated separately, allowing to model covariance across time more easily. This last point is often disregarded when estimating mixture models. This study focused on the consequences of a misspecified covariance matrix when estimating the number of groups in a mixture. The main consequence is an overestimation of the number of groups, i.e. we estimate groups that do not exist. In particular, the independence assumption of the observations across time when they were in fact correlated resulted in estimating many non existing groups. This overestimation of the number of groups also resulted in an overfit of the model, i.e. we used more parameters than necessary. Finally mixture models were fitted to behavioral and cognitive data. We fitted the data first assuming a covariance structure, then assuming independence. In most cases, the analyses conducted assuming a covariance structure ended up having fewer groups and the results were simpler and clearer to interpret.